Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Mahmood, Hanya; Shaban, Muhammad; Rajpoot, Nasir M.; Khurram, Syed Ali

doi:10.1038/s41416-021-01386-x

Cited by 83 publications

(79 citation statements)

References 59 publications

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“…In medicine, especially in cancer management, primary diagnostic imaging modalities are fed into deep learning to achieve a trained model that can offer effective prognostication of cancer outcomes [ 44 ]. Examples of these highly dimensioned mineable primary diagnostic imaging modalities include radiomic data, ultrasound (US), computed tomography (CT), genomic, magnetic resonance imaging (MRI), and positron emission tomography (PET) [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Alabi¹,

Almangush²,

Elmusrati³

et al. 2022

Front. Oral. Health

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Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

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Section: Discussionmentioning

confidence: 99%

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Alabi¹,

Almangush²,

Elmusrati³

et al. 2022

Front. Oral. Health

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“…Regarding the future scope of CLE, transference of the first tentative results of CLE in the human oral cavity into an effective and evidence based clinical setting will be a crucial step. Recent research has shown an evolution of artificial intelligence algorithms and the utilization of computational methods for an accurate diagnosis and prognosis of head and neck cancers [ 67 ]. Artificial intelligence science along with precision-based optical imaging systems such as confocal microscopy greatly improve the prospects of improving screening and prognostic outcomes of OSCC.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Confocal Laser Endomicroscopy for the Diagnosis of Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

Sethi

Logan

et al. 2021

IJERPH

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Background: Advances in treatment approaches for patients with oral squamous cell carcinoma (OSCC) have been unsuccessful in preventing frequent recurrences and distant metastases, leading to a poor prognosis. Early detection and prevention enable an improved 5-year survival and better prognosis. Confocal Laser Endomicroscopy (CLE) is a non-invasive imaging instrument that could enable an earlier diagnosis and possibly help in reducing unnecessary invasive surgical procedures. Objective: To present an up to date systematic review and meta-analysis assessing the diagnostic accuracy of CLE in diagnosing OSCC. Materials and Methods. PubMed, Scopus, and Web of Science databases were explored up to 30 June 2021, to collect articles concerning the diagnosis of OSCC through CLE. Screening: data extraction and appraisal was done by two reviewers. The quality of the methodology followed by the studies included in this review was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A random effects model was used for the meta-analysis. Results: Six studies were included, leading to a total number of 361 lesions in 213 patients. The pooled sensitivity and specificity were 95% (95% CI, 92–97%; I2 = 77.5%) and 93% (95% CI, 90–95%; I2 = 68.6%); the pooled positive likelihood ratios and negative likelihood ratios were 10.85 (95% CI, 5.4–21.7; I2 = 55.9%) and 0.08 (95% CI, 0.03–0.2; I2 = 83.5%); and the pooled diagnostic odds ratio was 174.45 (95% CI, 34.51–881.69; I2 = 73.6%). Although risk of bias and heterogeneity is observed, this study validates that CLE may have a noteworthy clinical influence on the diagnosis of OSCC, through its high sensitivity and specificity. Conclusions: This review indicates an exceptionally high sensitivity and specificity of CLE for diagnosing OSCC. Whilst it is a promising diagnostic instrument, the limited number of existing studies and potential risk of bias of included studies does not allow us to draw firm conclusions. A conclusive inference can be drawn when more studies, possibly with homogeneous methodological approach, are performed.

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“…Indeed, improvements in the accuracy of prediction could greatly assist healthcare professionals in early detection and planning optimal patient-specific treatments to reduce the burden of disease. However, a recent systematic review that analyses and describes the application and diagnostic accuracy of AI methods for detection and grading of pre-cancerous and cancerous head and neck lesions identifies a lack of evidence for these methods [ 80 ]. Authors highlight that the overall quality of evidence in studies is low, mainly due to the use of small, unicentric data sets and a high risk of bias that could have overestimated model accuracy rates.…”

Section: Artificial Intelligencementioning

confidence: 99%

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

et al. 2021

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The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.

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Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Cited by 83 publications

References 59 publications

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Diagnostic Accuracy of Confocal Laser Endomicroscopy for the Diagnosis of Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

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